This report uses IRC’s monitoring data to present a summary of the program implementation and overall compliance.

Code

# Mainly use irc monitoring data
irc_monitoring <- read_dta("reBuild_Kenya/DataConstruction/2_build/irc_monitoring.dta") %>%
  select(caseid, site, treatment2, control_status, treatment, total_payments, all_payments_count, all_payments_types, contains("payment_date"), contains("payment_type"), day) %>%
  # We create a treated variable for people considered treated/enrolled/compliant by the IRC
  mutate(treated = case_when(treatment2 == 4 & control_status!=1 ~ 0,
                             .default = 1)) %>%
  # We clean the infosession dates : the Kawangware and Pangani dates are recorded as 2023 instead of 2022
  mutate(infosesh_date = case_when(site == 2 ~ day - 365, #Kawangware
                                   site == 1 ~ day - 365, #Pangani
                                   .default = day)) %>%
  select(-day)

# Read in panel data
final_panel <- read_dta("reBuild_Kenya/DataConstruction/2_build/final_panel.dta") %>%
  filter(obs == 1) # Only keep observed rows

# Read in registration data
registration_date <- read_dta("reBuild_Kenya/DataConstruction/2_build/registration.date.dta")

Code

## Clean the final panel data
final_panel_clean <- final_panel %>%
  select(caseid, cohort, gender, mentee, refugee, starttime, treat_assignment, final_assignment, obs, panel_round)

## Transform into date format
final_panel_clean$starttime <- as.character(final_panel_clean$starttime) # Make sure it's character
final_panel_clean$startdate_only <- sub(" .*", "", final_panel_clean$starttime) # Delete everything after the space
final_panel_clean$startdate_only <- as.Date(final_panel_clean$startdate_only, format = "%Y-%m-%d") # Transform into date format

# Transform panel data from long to wide
# Every row is unique caseid
final_panel_wide <- final_panel_clean %>%
  pivot_wider(names_from = panel_round, 
              values_from = startdate_only,
              id_cols = c(caseid, cohort, gender, mentee, refugee, treat_assignment, final_assignment, obs)) %>%
  rename(Baseline_startdate = "1",
         FU1_startdate = "2",
         MID3_startdate = "3",
         MID6_startdate = "4",
         END9_startdate = "5"
  )

## Add a treatment-treated-control variable to the final panel 

final_panel_wide <- final_panel_wide %>%
  #add the monitoring data to determine treated status
  left_join(irc_monitoring, by = "caseid") %>%
  #if an observation isn't in the monitoring data, it is untreated
  mutate(treated = case_when(is.na(treated) ~ 0,
                             .default = treated),
  #create three groups: control, untreated, treated (default)
         group = case_when(treat_assignment == 0 ~ 3,
                           treat_assignment != 0 & treated == 0 ~ 2,
                           .default = 1),
  #create the demographic breakdown variable
         demo = case_when(gender == "Male" & refugee == 0 ~ 1,
                          gender == "Female" & refugee == 0 ~ 2,
                          gender == "Male" & refugee == 1 ~ 3,
                          gender == "Female" & refugee == 1 ~ 4),
          female = case_when(gender == "Male" ~ 0,
                             gender == "Female" ~ 1))

## Append registration date to the final panel

final_panel_wide <- final_panel_wide %>%
  left_join(registration_date, by = "caseid")

final_panel_wide$Fullregistrationdate = as.Date(final_panel_wide$Fullregistrationdate, format = "%Y-%m-%d")

## Store the group and demographic labels for graph labeling
grouplabels <- c("Treated", "Untreated", "Control")
demolabels <- c("Male Host", "Female Host", "Male Refugee", "Female Refugee")
femalelabels <- c("Male", "Female")
refugeelabels <- c("Host", "Refugee")

# treatment2: 1 = Cash, 2 = BC, 3 = PS, 4 = control

Program Compliance

Below, we define compliance as being considered enrolled in the program by IRC, aka having showed up for the initial info session required to start programming. From the perspective of the control group, being non-compliant means not having picked up the assignment call.

Code

# Seperate mentee and mentor Mentee data
final_panel_wide_mentee <- final_panel_wide %>%
    filter(mentee == 1) %>%
    group_by(final_assignment, treated) %>%
    count() %>%
    group_by(final_assignment) %>%
    mutate(total_n = sum(n), percentage = round((n/total_n) * 100, 2)) %>%
    mutate(percentage = paste0(percentage, "%")) %>%
    filter(treated == 1) %>%
    select(final_assignment, total_n, n, percentage)

# Mentor data
final_panel_wide_mentor <- final_panel_wide %>%
    filter(mentee == 0) %>%
    group_by(final_assignment, treated) %>%
    count() %>%
    group_by(final_assignment) %>%
    mutate(total_n = sum(n), percentage = round((n/total_n) * 100, 2)) %>%
    mutate(percentage = paste0(percentage, "%")) %>%
    filter(treated == 1) %>%
    select(final_assignment, total_n, n, percentage)

## Mentee Table Set up row names and column names
rownames(final_panel_wide_mentee) <- c("Cash", "Business Content", "Perspective Sharing",
    "Control")
final_panel_wide_mentee$final_assignment <- rownames(final_panel_wide_mentee)
colnames(final_panel_wide_mentee) <- c("Assignment", "N Assigned", "N Compliant",
    "% Compliant")

# Use kable to create a table with the results
kable(final_panel_wide_mentee, format = "html", caption = "Compliant Mentees", row.names = FALSE) %>%
    kable_styling(font_size = 9, full_width = F, bootstrap_options = c("striped")) %>%
    column_spec(1, width = "3cm") %>%
    column_spec(2, width = "2cm")
## Mentor Table Set up row names and column names
rownames(final_panel_wide_mentor) <- c("Business Content", "Perspective Sharing",
    "Control")
final_panel_wide_mentor$final_assignment <- rownames(final_panel_wide_mentor)
colnames(final_panel_wide_mentor) <- c("Assignment", "N Assigned", "N Compliant",
    "% Compliant")

# Use kable to create a table with the results
kable(final_panel_wide_mentor, format = "html", caption = "Compliant Mentors", row.names = FALSE) %>%
    kable_styling(font_size = 9, full_width = F, bootstrap_options = c("striped")) %>%
    column_spec(1, width = "3cm") %>%
    column_spec(2, width = "2cm")

Compliant Mentees

Assignment	N Assigned	N Compliant	% Compliant
Cash	606	569	93.89%
Business Content	400	388	97%
Perspective Sharing	398	389	97.74%
Control	619	589	95.15%

Compliant Mentors

Assignment	N Assigned	N Compliant	% Compliant
Business Content	611	579	94.76%
Perspective Sharing	656	595	90.7%
Control	635	589	92.76%

Payments Compliance

In this section, we look at compliance with the planned payments scheduled, limiting the sample to people who looking at the people officially as per the IRC

Code

## Mentee Payment
## Show business grant only for mentees

mentee_payment <- final_panel_wide %>%
  filter(treated == 1, # Filter to active treatment arms only
         mentee == 1, # Filter to mentee only
         final_assignment != 'control') %>% # Filter to people who get treatment
  mutate(
    receive_any_payment = ifelse(total_payments > 0, 1, 0),
    business_grant = ifelse(str_detect(all_payments_types, "business_grant"), 1, 0)
  ) %>%
  group_by(final_assignment) %>%
  summarize(
    num = n(),
    num_receive_any_payment = sum(receive_any_payment),
    num_business_grant = sum(business_grant),
    percent_receive_any_payment = round((num_receive_any_payment / num) * 100, 2),
    percent_business_grant = round((num_business_grant / num) * 100, 2),
    average_num_payments = round(mean(all_payments_count, na.rm = TRUE),2),
    average_amount_payments = round(mean(total_payments, na.rm = TRUE),2)
  ) %>%
  select(final_assignment, num, percent_receive_any_payment, percent_business_grant, average_num_payments, average_amount_payments)

## Mentor Payment
mentor_payment <- final_panel_wide %>%
  filter(treated == 1, # Filter to active treatment arms only
         mentee == 0, # Filter to mentor only
         final_assignment != 'control') %>% # Filter to people who get treatment
  mutate(
    receive_any_payment = ifelse(total_payments > 0, 1, 0)
  ) %>%
  group_by(final_assignment) %>%
  summarize(
    num = n(),
    num_receive_any_payment = sum(receive_any_payment),
    percent_receive_any_payment = round((num_receive_any_payment / num) * 100, 2),
    average_num_payments = round(mean(all_payments_count, na.rm = TRUE),2),
    average_amount_payments = round(mean(total_payments, na.rm = TRUE),2)
  ) %>%
  select(final_assignment, num, percent_receive_any_payment, average_num_payments, average_amount_payments)

Code

## Mentee Payment Table Set up row names and column names
rownames(mentee_payment) <- c("Business Content", "Cash", "Perspective Sharing")
mentee_payment$final_assignment <- rownames(mentee_payment)
colnames(mentee_payment) <- c("Assignment", "N", "% got any payment", "% got business grant",
    "Average number of payments received", "Average total amount received (KES)")

# Use kable to create a table with the results
kable(mentee_payment, format = "html", caption = "Mentees Payment", row.names = FALSE) %>%
    kable_styling(font_size = 9, full_width = F, bootstrap_options = c("striped")) %>%
    column_spec(1, width = "3cm") %>%
    column_spec(2, width = "2cm") %>%
    column_spec(3, width = "2cm") %>%
    column_spec(4, width = "2cm") %>%
    column_spec(5, width = "2cm") %>%
    column_spec(6, width = "2cm")
## Mentor Table Set up row names and column names
rownames(mentor_payment) <- c("Business Content", "Perspective Sharing")
mentor_payment$final_assignment <- rownames(mentor_payment)
colnames(mentor_payment) <- c("Assignment", "N", "% got any payment", "Average number of payments received",
    "Average total amount received (KES)")

# Use kable to create a table with the results
kable(mentor_payment, format = "html", caption = "Mentors Payment", row.names = FALSE) %>%
    kable_styling(font_size = 9, full_width = F, bootstrap_options = c("striped")) %>%
    column_spec(1, width = "3cm") %>%
    column_spec(2, width = "2cm") %>%
    column_spec(3, width = "2cm") %>%
    column_spec(4, width = "2cm") %>%
    column_spec(5, width = "2cm")

Mentees Payment

Assignment	N	% got any payment	% got business grant	Average number of payments received	Average total amount received (KES)
Business Content	569	100	99.47	4.27	59887.17
Cash	388	100	100.00	1.98	56397.94
Perspective Sharing	589	100	100.00	4.26	60068.93

Mentors Payment

Assignment	N	% got any payment	Average number of payments received	Average total amount received (KES)
Business Content	579	100	3.24	25242.14
Perspective Sharing	589	100	3.25	25343.29

Study Milestones Timeline

Code

# Calculate for each survey round the distance to baseline (in days)
# Calculate the distance between registration and baseline (in days, should be negative)

final_panel_wide <- final_panel_wide %>%
  mutate(
    distance_to_baseline_fu1 = as.numeric(as.Date(FU1_startdate) - as.Date(Baseline_startdate)),
    distance_to_baseline_mid3 = as.numeric(as.Date(MID3_startdate) - as.Date(Baseline_startdate)),
    distance_to_baseline_mid6 = as.numeric(as.Date(MID6_startdate) - as.Date(Baseline_startdate)),
    distance_to_baseline_end9 = as.numeric(as.Date(END9_startdate) - as.Date(Baseline_startdate)),
    distance_to_baseline_reg = as.numeric(as.Date(Fullregistrationdate) - as.Date(Baseline_startdate)),
    
    ## Check if it is NA 
    
    distance_to_baseline_fu1 = ifelse(distance_to_baseline_fu1 < 0, NA, distance_to_baseline_fu1),
    distance_to_baseline_mid3 = ifelse(distance_to_baseline_mid3 < 0, NA, distance_to_baseline_mid3),
    distance_to_baseline_mid6 = ifelse(distance_to_baseline_mid6 < 0, NA, distance_to_baseline_mid6),
    distance_to_baseline_end9 = ifelse(distance_to_baseline_end9 < 0, NA, distance_to_baseline_end9)
  )

final_panel_wide <- final_panel_wide %>%
  mutate(across(starts_with("payment_date_"), as.Date)) %>% # Convert to date
  mutate(
    first_payment_date = pmin(payment_date_1, payment_date_2, payment_date_3, payment_date_4, 
                              payment_date_5, payment_date_6, payment_date_7, na.rm = TRUE),
    last_payment_date = pmax(payment_date_1, payment_date_2, payment_date_3, payment_date_4, 
                             payment_date_5, payment_date_6, payment_date_7, na.rm = TRUE)
  )

Code

# Find the business grant payment dates
# Check from payment_type_1 to payment_type_7 if it is business grant, find the payment date sharing the same suffix

final_panel_wide <- final_panel_wide %>%
  # Find business grant payment dates
  mutate(
    # Assuming "business_grant" is the exact phrase to match in payment_type columns
    business_grant_dates = pmin(
      case_when(payment_type_1 == "business_grant" ~ payment_date_1, TRUE ~ as.Date(NA)),
      case_when(payment_type_2 == "business_grant" ~ payment_date_2, TRUE ~ as.Date(NA)),
      case_when(payment_type_3 == "business_grant" ~ payment_date_3, TRUE ~ as.Date(NA)),
      case_when(payment_type_4 == "business_grant" ~ payment_date_4, TRUE ~ as.Date(NA)),
      case_when(payment_type_5 == "business_grant" ~ payment_date_5, TRUE ~ as.Date(NA)),
      case_when(payment_type_6 == "business_grant" ~ payment_date_6, TRUE ~ as.Date(NA)),
      case_when(payment_type_7 == "business_grant" ~ payment_date_7, TRUE ~ as.Date(NA)),
      na.rm = TRUE
    )
  )

# Add time to business grant payment 

final_panel_wide <- final_panel_wide %>%
  mutate(
    distance_to_bizgrant_reg = as.numeric(as.Date(Fullregistrationdate) - as.Date(business_grant_dates)),
    distance_to_bizgrant_base = as.numeric(as.Date(Baseline_startdate) - as.Date(business_grant_dates)),
    distance_to_bizgrant_fu1 = as.numeric(as.Date(FU1_startdate) - as.Date(business_grant_dates)),
    distance_to_bizgrant_mid3 = as.numeric(as.Date(MID3_startdate) - as.Date(business_grant_dates)),
    distance_to_bizgrant_mid6 = as.numeric(as.Date(MID6_startdate) - as.Date(business_grant_dates)),
    distance_to_bizgrant_end9 = as.numeric(as.Date(END9_startdate) - as.Date(business_grant_dates)),
  )

# Add time to info session

final_panel_wide <- final_panel_wide %>%
  mutate(
    distance_to_infosesh_reg = as.numeric(as.Date(Fullregistrationdate) - as.Date(infosesh_date)),
    distance_to_infosesh_base = as.numeric(as.Date(Baseline_startdate) - as.Date(infosesh_date)),
    distance_to_infosesh_fu1 = as.numeric(as.Date(FU1_startdate) - as.Date(infosesh_date)),
    distance_to_infosesh_mid3 = as.numeric(as.Date(MID3_startdate) - as.Date(infosesh_date)),
    distance_to_infosesh_mid6 = as.numeric(as.Date(MID6_startdate) - as.Date(infosesh_date)),
    distance_to_infosesh_end9 = as.numeric(as.Date(END9_startdate) - as.Date(infosesh_date)),
  )

# Add distance to baseline for bizgrant and infosession to add to the main timeline graph

final_panel_wide <- final_panel_wide %>%
  mutate(
    distance_to_baseline_bizgrant = as.numeric(as.Date(business_grant_dates) - as.Date(Baseline_startdate)),
    distance_to_baseline_infosesh = as.numeric(as.Date(infosesh_date) - as.Date(Baseline_startdate))
  )

Timeline by Cohort

Mentee Timeline

Code

# Plot the average over time of each round | layout: true | .widths: [50, 50] |
# .heights: [50, 50] Use line graph to show the average distance to baseline
# over time, different lines for different cohorts Use
# distance_to_baseline_fu1, distance_to_baseline_mid3,
# distance_to_baseline_mid6, distance_to_baseline_end9 Use the average of each
# round

# Transform 'cohort' to factor
final_panel_wide$cohort <- as_factor(final_panel_wide$cohort)

final_panel_wide_mentee_timeline <- final_panel_wide %>%
    filter(mentee == 1) %>%
    group_by(cohort, refugee) %>%
    summarize(avg_distance_to_baseline_reg = mean(distance_to_baseline_reg, na.rm = TRUE),
        avg_distance_to_baseline_fu1 = mean(distance_to_baseline_fu1, na.rm = TRUE),
        avg_distance_to_baseline_mid3 = mean(distance_to_baseline_mid3, na.rm = TRUE),
        avg_distance_to_baseline_mid6 = mean(distance_to_baseline_mid6, na.rm = TRUE),
        avg_distance_to_baseline_end9 = mean(distance_to_baseline_end9, na.rm = TRUE),
        avg_distance_to_baseline_bizgrant = mean(distance_to_baseline_bizgrant, na.rm = TRUE),
        avg_distance_to_baseline_infosesh = mean(distance_to_baseline_infosesh, na.rm = TRUE),
        .groups = "drop") %>%
    pivot_longer(cols = starts_with("avg_distance_to_baseline_"), names_to = "round",
        values_to = "avg_distance_to_baseline")

final_panel_wide_mentee_timeline$round <- fct_relevel(final_panel_wide_mentee_timeline$round,
    "avg_distance_to_baseline_reg", "avg_distance_to_baseline_base", "avg_distance_to_baseline_infosesh",
    "avg_distance_to_baseline_fu1", "avg_distance_to_baseline_bizgrant", "avg_distance_to_baseline_mid3",
    "avg_distance_to_baseline_mid6", "avg_distance_to_baseline_end9")

# Mentee Timeline Plot Seperate by refugee or host
final_panel_wide_mentee_timeline %>%
    mutate(refugee = ifelse(refugee == 1, "Refugee", "Host")) %>%
    ggplot(aes(x = round, y = avg_distance_to_baseline, color = cohort, group = cohort)) +
    geom_point() + geom_line() + coord_flip() + labs(x = "Round", y = "Average distance to baseline (in days)",
    title = "Average distance to baseline over time(Mentee)", subtitle = "Average distance to baseline over time, shown by cohort") +
    scale_x_discrete(labels = c("Registration", "Info Session", "Follow-up 1", "Business Grant",
        "Midline 3", "Midline 6", "Endline 9")) + geom_hline(yintercept = 0, linetype = "dashed",
    color = "red") + theme_minimal() + facet_wrap(~refugee)

Mentor Timeline

Code

# Plot the average over time of each round | layout: true | .widths: [50, 50] |
# .heights: [50, 50]

final_panel_wide_mentor_timeline <- final_panel_wide %>%
    filter(mentee == 0) %>%
    group_by(cohort, refugee) %>%
    summarize(avg_distance_to_baseline_reg = mean(distance_to_baseline_reg, na.rm = TRUE),
        avg_distance_to_baseline_fu1 = mean(distance_to_baseline_fu1, na.rm = TRUE),
        avg_distance_to_baseline_mid3 = mean(distance_to_baseline_mid3, na.rm = TRUE),
        avg_distance_to_baseline_mid6 = mean(distance_to_baseline_mid6, na.rm = TRUE),
        avg_distance_to_baseline_end9 = mean(distance_to_baseline_end9, na.rm = TRUE),
        avg_distance_to_baseline_infosesh = mean(distance_to_baseline_infosesh, na.rm = TRUE),
        .groups = "drop") %>%
    pivot_longer(cols = starts_with("avg_distance_to_baseline_"), names_to = "round",
        values_to = "avg_distance_to_baseline")

final_panel_wide_mentor_timeline$round <- fct_relevel(final_panel_wide_mentor_timeline$round,
    "avg_distance_to_baseline_reg", "avg_distance_to_baseline_base", "avg_distance_to_baseline_infosesh",
    "avg_distance_to_baseline_fu1", "avg_distance_to_baseline_mid3", "avg_distance_to_baseline_mid6",
    "avg_distance_to_baseline_end9")

# Mentor Timeline Plot
final_panel_wide_mentor_timeline %>%
    mutate(refugee = ifelse(refugee == 1, "Refugee", "Host")) %>%
    ggplot(aes(x = round, y = avg_distance_to_baseline, color = cohort, group = cohort)) +
    geom_point() + geom_line() + coord_flip() + labs(x = "Round", y = "Average distance to baseline (in days)",
    title = "Average distance to baseline over time(Mentor)", subtitle = "Average distance to baseline over time, shown by cohort") +
    scale_x_discrete(labels = c("Registration", "Info Session", "Follow-up 1", "Midline 3",
        "Midline 6", "Endline 9")) + geom_hline(yintercept = 0, linetype = "dashed",
    color = "red") + theme_minimal() + facet_wrap(~refugee)

Timeline by Active Treatment Arm

Mentee Timeline

Code

# Plot the average over time of each round | layout: true | .widths: [50, 50] |
# .heights: [50, 50] Use line graph to show the average distance to baseline
# over time, different lines for different treatment arms


# Transform 'final_assignment' to factor
final_panel_wide$final_assignment <- as_factor(final_panel_wide$final_assignment)

final_panel_wide_mentee_timeline_arm <- final_panel_wide %>%
    filter(mentee == 1, final_assignment != "control") %>%
    group_by(final_assignment) %>%
    summarize(avg_distance_to_baseline_reg = mean(distance_to_baseline_reg, na.rm = TRUE),
        avg_distance_to_baseline_fu1 = mean(distance_to_baseline_fu1, na.rm = TRUE),
        avg_distance_to_baseline_mid3 = mean(distance_to_baseline_mid3, na.rm = TRUE),
        avg_distance_to_baseline_mid6 = mean(distance_to_baseline_mid6, na.rm = TRUE),
        avg_distance_to_baseline_end9 = mean(distance_to_baseline_end9, na.rm = TRUE),
        avg_distance_to_baseline_bizgrant = mean(distance_to_baseline_bizgrant, na.rm = TRUE),
        avg_distance_to_baseline_infosesh = mean(distance_to_baseline_infosesh, na.rm = TRUE),
        .groups = "drop") %>%
    pivot_longer(cols = starts_with("avg_distance_to_baseline_"), names_to = "round",
        values_to = "avg_distance_to_baseline")

final_panel_wide_mentee_timeline_arm$round <- fct_relevel(final_panel_wide_mentee_timeline_arm$round,
    "avg_distance_to_baseline_reg", "avg_distance_to_baseline_base", "avg_distance_to_baseline_infosesh",
    "avg_distance_to_baseline_fu1", "avg_distance_to_baseline_bizgrant", "avg_distance_to_baseline_mid3",
    "avg_distance_to_baseline_mid6", "avg_distance_to_baseline_end9")

# Mentee Timeline Plot Seperate by active treatment arm

final_panel_wide_mentee_timeline_arm %>%
    mutate(final_assignment = ifelse(final_assignment == "bc", "Business Content",
        ifelse(final_assignment == "pg", "Perspective Sharing", ifelse(final_assignment ==
            "cash", "Cash", final_assignment)))) %>%
    ggplot(aes(x = round, y = avg_distance_to_baseline, color = final_assignment,
        group = final_assignment)) + geom_point() + geom_line() + coord_flip() +
    labs(x = "Round", y = "Average distance to baseline (in days)", title = "Average distance to baseline over time(Mentee)",
        subtitle = "Average distance to baseline over time, shown by active treatment arm") +
    scale_x_discrete(labels = c("Registration", "Info Session", "Follow-up 1", "Business Grant",
        "Midline 3", "Midline 6", "Endline 9")) + geom_hline(yintercept = 0, linetype = "dashed",
    color = "red") + theme_minimal()

Mentor Timeline

Code

# Plot the average over time of each round | layout: true | .widths: [50, 50] |
# .heights: [50, 50]

# Use line graph to show the average distance to baseline over time, different
# lines for different treatment arms

final_panel_wide_mentor_timeline_arm <- final_panel_wide %>%
    filter(mentee == 0, final_assignment != "control") %>%
    group_by(final_assignment) %>%
    summarize(avg_distance_to_baseline_reg = mean(distance_to_baseline_reg, na.rm = TRUE),
        avg_distance_to_baseline_fu1 = mean(distance_to_baseline_fu1, na.rm = TRUE),
        avg_distance_to_baseline_mid3 = mean(distance_to_baseline_mid3, na.rm = TRUE),
        avg_distance_to_baseline_mid6 = mean(distance_to_baseline_mid6, na.rm = TRUE),
        avg_distance_to_baseline_end9 = mean(distance_to_baseline_end9, na.rm = TRUE),
        avg_distance_to_baseline_infosesh = mean(distance_to_baseline_infosesh, na.rm = TRUE),
        .groups = "drop") %>%
    pivot_longer(cols = starts_with("avg_distance_to_baseline_"), names_to = "round",
        values_to = "avg_distance_to_baseline")

final_panel_wide_mentor_timeline_arm$round <- fct_relevel(final_panel_wide_mentor_timeline_arm$round,
    "avg_distance_to_baseline_reg", "avg_distance_to_baseline_base", "avg_distance_to_baseline_infosesh",
    "avg_distance_to_baseline_fu1", "avg_distance_to_baseline_mid3", "avg_distance_to_baseline_mid6",
    "avg_distance_to_baseline_end9")

# Mentee Timeline Plot Seperate by active treatment arm

final_panel_wide_mentor_timeline_arm %>%
    mutate(final_assignment = ifelse(final_assignment == "bc", "Business Content",
        ifelse(final_assignment == "pg", "Perspective Sharing", final_assignment))) %>%
    ggplot(aes(x = round, y = avg_distance_to_baseline, color = final_assignment,
        group = final_assignment)) + geom_point() + geom_line() + coord_flip() +
    labs(x = "Round", y = "Average distance to baseline (in days)", title = "Average distance to baseline over time(Mentor)",
        subtitle = "Average distance to baseline over time, shown by active treatment arm") +
    scale_x_discrete(labels = c("Registration", "Info Session", "Follow-up 1", "Midline 3",
        "Midline 6", "Endline 9")) + geom_hline(yintercept = 0, linetype = "dashed",
    color = "red") + theme_minimal()

Study Milestone Time Ranges

Time Ranges by cohort

Mentee Refugee

Code

# Use boxplot to plot the range over time of each round Use
# distance_to_baseline_fu1, distance_to_baseline_mid3,
# distance_to_baseline_mid6, distance_to_baseline_end9

final_panel_long_mentee_timerange <- final_panel_wide %>%
    filter(mentee == 1) %>%
    group_by(refugee, cohort) %>%
    pivot_longer(cols = starts_with("distance_to_baseline_"), names_to = "round",
        values_to = "distance_to_baseline") %>%
    mutate(round = factor(round, levels = c("distance_to_baseline_reg", "distance_to_baseline_infosesh",
        "distance_to_baseline_fu1", "distance_to_baseline_bizgrant", "distance_to_baseline_mid3",
        "distance_to_baseline_mid6", "distance_to_baseline_end9")))

Code

# Plot the boxplot
final_panel_long_mentee_timerange %>%
    filter(refugee == 1) %>%
    ggplot(aes(x = round, y = distance_to_baseline, color = cohort)) + geom_boxplot() +
    coord_flip() + labs(x = "Round", y = "Distance to baseline (in days)", title = "Range of distance to baseline over time(Mentee Refugee)",
    subtitle = "Each round's distance to baseline") + geom_hline(yintercept = 0,
    linetype = "dashed", color = "red") + scale_x_discrete(labels = c("Registration",
    "Info Session", "Follow-up 1", "Business Grant", "Midline 3", "Midline 6", "Endline 9")) +
    theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Mentee Host

Code

# Plot the boxplot
final_panel_long_mentee_timerange %>%
    filter(refugee == 0) %>%
    ggplot(aes(x = round, y = distance_to_baseline, color = cohort)) + geom_boxplot() +
    coord_flip() + labs(x = "Round", y = "Distance to baseline (in days)", title = "Range of distance to baseline over time(Mentee Host)",
    subtitle = "Each round's distance to baseline") + geom_hline(yintercept = 0,
    linetype = "dashed", color = "red") + scale_x_discrete(labels = c("Registration",
    "Info Session", "Follow-up 1", "Business Grant", "Midline 3", "Midline 6", "Endline 9")) +
    theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Mentor

Code

final_panel_long_mentor_timerange <- final_panel_wide %>%
    filter(mentee == 0) %>%
    # remove the distance to baseline bizgrant var to avoid creating NAs when
    # pivoting
select(-distance_to_baseline_bizgrant) %>%
    group_by(refugee, cohort) %>%
    pivot_longer(cols = starts_with("distance_to_baseline_"), names_to = "round",
        values_to = "distance_to_baseline") %>%
    mutate(round = factor(round, levels = c("distance_to_baseline_reg", "distance_to_baseline_infosesh",
        "distance_to_baseline_fu1", "distance_to_baseline_mid3", "distance_to_baseline_mid6",
        "distance_to_baseline_end9")))

final_panel_long_mentor_timerange %>%
    filter(refugee == 0) %>%
    ggplot(aes(x = round, y = distance_to_baseline, color = cohort)) + geom_boxplot() +
    coord_flip() + labs(x = "Round", y = "Distance to baseline (in days)", title = "Range of distance to baseline over time(Mentor Host)",
    subtitle = "Each round's distance to baseline") + geom_hline(yintercept = 0,
    linetype = "dashed", color = "red") + scale_x_discrete(labels = c("Registration",
    "Info Session", "Follow-up 1", "Midline 3", "Midline 6", "Endline 9")) + theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Time Range by Treatment Arm

Mentee Refugee

Code

# Use boxplot to plot the range over time of each round Use
# distance_to_baseline_fu1, distance_to_baseline_mid3,
# distance_to_baseline_mid6, distance_to_baseline_end9

final_panel_long_mentee_timerange_arm <- final_panel_wide %>%
    filter(mentee == 1) %>%
    group_by(refugee, final_assignment) %>%
    pivot_longer(cols = starts_with("distance_to_baseline_"), names_to = "round",
        values_to = "distance_to_baseline") %>%
    mutate(round = factor(round, levels = c("distance_to_baseline_reg", "distance_to_baseline_infosesh",
        "distance_to_baseline_fu1", "distance_to_baseline_bizgrant", "distance_to_baseline_mid3",
        "distance_to_baseline_mid6", "distance_to_baseline_end9")))

Code

# Plot the boxplot
final_panel_long_mentee_timerange_arm %>%
    filter(refugee == 1, final_assignment != "control") %>%
    mutate(final_assignment = ifelse(final_assignment == "bc", "Business Content",
        ifelse(final_assignment == "pg", "Perspective Sharing", ifelse(final_assignment ==
            "cash", "Cash", final_assignment)))) %>%
    ggplot(aes(x = round, y = distance_to_baseline, color = final_assignment)) +
    geom_boxplot() + coord_flip() + labs(x = "Round", y = "Distance to baseline (in days)",
    title = "Range of distance to baseline over time(Mentee Refugee)", subtitle = "Each round's distance to baseline") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "red") + scale_x_discrete(labels = c("Registration",
    "Info Session", "Follow-up 1", "Business Grant", "Midline 3", "Midline 6", "Endline 9")) +
    theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Mentee Host

Code

# Plot the boxplot
final_panel_long_mentee_timerange_arm %>%
    filter(refugee == 0, final_assignment != "control") %>%
    mutate(final_assignment = ifelse(final_assignment == "bc", "Business Content",
        ifelse(final_assignment == "pg", "Perspective Sharing", ifelse(final_assignment ==
            "cash", "Cash", final_assignment)))) %>%
    ggplot(aes(x = round, y = distance_to_baseline, color = final_assignment)) +
    geom_boxplot() + coord_flip() + labs(x = "Round", y = "Distance to baseline (in days)",
    title = "Range of distance to baseline over time(Mentee Host)", subtitle = "Each round's distance to baseline") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "red") + scale_x_discrete(labels = c("Registration",
    "Info Session", "Follow-up 1", "Business Grant", "Midline 3", "Midline 6", "Endline 9")) +
    theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Mentor

Code

final_panel_long_mentor_timerange_arm <- final_panel_wide %>%
    filter(mentee == 0) %>%
    # remove the distance to baseline bizgrant var to avoid creating NAs when
    # pivoting
select(-distance_to_baseline_bizgrant) %>%
    group_by(refugee, final_assignment) %>%
    pivot_longer(cols = starts_with("distance_to_baseline_"), names_to = "round",
        values_to = "distance_to_baseline") %>%
    mutate(round = factor(round, levels = c("distance_to_baseline_reg", "distance_to_baseline_infosesh",
        "distance_to_baseline_fu1", "distance_to_baseline_mid3", "distance_to_baseline_mid6",
        "distance_to_baseline_end9")))

final_panel_long_mentor_timerange_arm %>%
    filter(refugee == 0, final_assignment != "control") %>%
    mutate(final_assignment = ifelse(final_assignment == "bc", "Business Content",
        ifelse(final_assignment == "pg", "Perspective Sharing", final_assignment))) %>%
    ggplot(aes(x = round, y = distance_to_baseline, color = final_assignment)) +
    geom_boxplot() + coord_flip() + labs(x = "Round", y = "Distance to baseline (in days)",
    title = "Range of distance to baseline over time(Mentor Host)", subtitle = "Each round's distance to baseline") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "red") + scale_x_discrete(labels = c("Registration",
    "Info Session", "Follow-up 1", "Midline 3", "Midline 6", "Endline 9")) + theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Payments Time Range

Payment Time Range by cohort

Mentee Business Grant Payment Time Range

Code

## 1.Active Treatment Arms
## 2. 
## Calculate distance to baseline for first, last, business grant payment dates
final_panel_wide <- final_panel_wide %>%
  mutate(
    distance_to_baseline_first = as.numeric(as.Date(first_payment_date) - as.Date(Baseline_startdate)),
    distance_to_baseline_last = as.numeric(as.Date(last_payment_date) - as.Date(Baseline_startdate)),
    distance_to_baseline_grant = as.numeric(as.Date(business_grant_dates) - as.Date(Baseline_startdate)),
    
    ## Check if it is NA 
    
    distance_to_baseline_last = ifelse(distance_to_baseline_last < 0, NA, distance_to_baseline_last),
    distance_to_baseline_grant = ifelse(distance_to_baseline_grant < 0, NA, distance_to_baseline_grant)
  )

final_panel_wide %>%
  filter(mentee == 1) %>%
  mutate(
    refugee = ifelse(refugee == 1, "Refugee", "Host")
  ) %>%
  ggplot(aes(y = distance_to_baseline_grant, color = cohort)) +
  geom_boxplot() +
  coord_flip() +
  labs(
    y = "Distance to baseline for Business Grant Payment (in days)",
    title = "Businee Grant Payment's distance to baseline",
    subtitle = "Range of distance to baseline over time for business grant payment(Mentee)"
  ) +
  scale_x_discrete(
    labels = c("Business Grant Payment")
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + # Rotate x-axis labels
  facet_wrap(~refugee, nrow = 2)

Mentor First and Last Payment Time Range

Code

final_panel_wide %>%
    filter(mentee == 0) %>%
    group_by(refugee, cohort) %>%
    pivot_longer(cols = c("distance_to_baseline_first", "distance_to_baseline_last"),
        names_to = "first_last", values_to = "distance_to_baseline_first_last") %>%
    mutate(first_last = factor(first_last, levels = c("distance_to_baseline_first",
        "distance_to_baseline_last"))) %>%
    ggplot(aes(x = first_last, y = distance_to_baseline_first_last, color = cohort)) +
    geom_boxplot() + coord_flip() + labs(x = "First and Last Payment", y = "Distance to baseline (in days)",
    title = "First and Last Payment's distance to baseline", subtitle = "Range of distance to baseline over time for first and last payments(Mentor Host)") +
    scale_x_discrete(labels = c("First Payment", "Last Payment")) + theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Payment Time Range by Active Treatment Arm

Mentee Business Grant Payment Time Range

Code

final_panel_wide %>%
  filter(mentee == 1,
         final_assignment != 'control') %>%
  mutate(
    refugee = ifelse(refugee == 1, "Refugee", "Host"),
    final_assignment = ifelse(
      final_assignment == "bc",
      "Business Content",
      ifelse(
        final_assignment == "pg",
        "Perspective Sharing",
        ifelse(
          final_assignment == "cash",
          "Cash", final_assignment
        )
        )
      )
  ) %>%
  ggplot(aes(y = distance_to_baseline_grant, color = final_assignment)) +
  geom_boxplot() +
  coord_flip() +
  labs(
    y = "Distance to baseline for Business Grant Payment (in days)",
    title = "Businee Grant Payment's distance to baseline",
    subtitle = "Range of distance to baseline over time for business grant payment(Mentee)"
  ) +
  scale_x_discrete(
    labels = c("Business Grant Payment")
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + # Rotate x-axis labels
  facet_wrap(~refugee, nrow = 2)

Mentor First and Last Payment Time Range

Code

final_panel_wide %>%
    filter(mentee == 0, final_assignment != "control") %>%
    group_by(refugee, final_assignment) %>%
    pivot_longer(cols = c("distance_to_baseline_first", "distance_to_baseline_last"),
        names_to = "first_last", values_to = "distance_to_baseline_first_last") %>%
    mutate(first_last = factor(first_last, levels = c("distance_to_baseline_first",
        "distance_to_baseline_last")), final_assignment = ifelse(final_assignment ==
        "bc", "Business Content", ifelse(final_assignment == "pg", "Perspective Sharing",
        final_assignment))) %>%
    ggplot(aes(x = first_last, y = distance_to_baseline_first_last, color = final_assignment)) +
    geom_boxplot() + coord_flip() + labs(x = "First and Last Payment", y = "Distance to baseline (in days)",
    title = "First and Last Payment's distance to baseline", subtitle = "Range of distance to baseline over time for first and last payments(Mentor Host)") +
    scale_x_discrete(labels = c("First Payment", "Last Payment")) + theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Treatment Exposure by Treatment Arms

Exposure since Info Session

Mentees

Code

# Use boxplot to plot the range over time of each round

exposure_mentees_infosesh <- final_panel_wide %>%
    filter(mentee == 1, final_assignment != "control") %>%
    group_by(final_assignment) %>%
    pivot_longer(cols = starts_with("distance_to_infosesh_"), names_to = "round",
        values_to = "distance_to_infosesh") %>%
    mutate(round = factor(round, levels = c("distance_to_infosesh_reg", "distance_to_infosesh_base",
        "distance_to_infosesh_fu1", "distance_to_infosesh_mid3", "distance_to_infosesh_mid6",
        "distance_to_infosesh_end9")))

Code

# Plot the boxplot
exposure_mentees_infosesh %>%
    mutate(final_assignment = ifelse(final_assignment == "bc", "Business Content",
        ifelse(final_assignment == "pg", "Perspective Sharing", ifelse(final_assignment ==
            "cash", "Cash", final_assignment)))) %>%
    ggplot(aes(x = round, y = distance_to_infosesh, color = final_assignment)) +
    geom_boxplot() + coord_flip() + labs(x = "Round", y = "Distance to info session (in days)",
    title = "Range of distance to info session over time (Mentees)", subtitle = "Each round's distance to info session") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "red") + scale_x_discrete(labels = c("Registration",
    "Baseline", "Follow-up 1", "Midline 3", "Midline 6", "Endline 9")) + theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Mentors

Code

exposure_mentors_infosesh <- final_panel_wide %>%
    filter(mentee == 0, final_assignment != "control") %>%
    group_by(final_assignment) %>%
    pivot_longer(cols = starts_with("distance_to_infosesh_"), names_to = "round",
        values_to = "distance_to_infosesh") %>%
    mutate(round = factor(round, levels = c("distance_to_infosesh_reg", "distance_to_infosesh_base",
        "distance_to_infosesh_fu1", "distance_to_infosesh_mid3", "distance_to_infosesh_mid6",
        "distance_to_infosesh_end9")))

exposure_mentors_infosesh %>%
    filter(final_assignment != "control") %>%
    mutate(final_assignment = ifelse(final_assignment == "bc", "Business Content",
        ifelse(final_assignment == "pg", "Perspective Sharing", final_assignment))) %>%
    ggplot(aes(x = round, y = distance_to_infosesh, color = final_assignment)) +
    geom_boxplot() + coord_flip() + labs(x = "Round", y = "Distance to info session (in days)",
    title = "Range of distance to info session over time (Mentor)", subtitle = "Each round's distance to info session") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "red") + scale_x_discrete(labels = c("Registration",
    "Info Session", "Follow-up 1", "Midline 3", "Midline 6", "Endline 9")) + theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels

Exposure since Business Grant (Mentees)

Code

# Use boxplot to plot the range over time of each round

exposure_mentees_biz_grant <- final_panel_wide %>%
    filter(mentee == 1, final_assignment != "control") %>%
    group_by(final_assignment) %>%
    pivot_longer(cols = starts_with("distance_to_bizgrant_"), names_to = "round",
        values_to = "distance_to_bizgrant") %>%
    mutate(round = factor(round, levels = c("distance_to_bizgrant_reg", "distance_to_bizgrant_base",
        "distance_to_bizgrant_fu1", "distance_to_bizgrant_mid3", "distance_to_bizgrant_mid6",
        "distance_to_bizgrant_end9")))

Code

# Plot the boxplot
exposure_mentees_biz_grant %>%
    mutate(final_assignment = ifelse(final_assignment == "bc", "Business Content",
        ifelse(final_assignment == "pg", "Perspective Sharing", ifelse(final_assignment ==
            "cash", "Cash", final_assignment)))) %>%
    ggplot(aes(x = round, y = distance_to_bizgrant, color = final_assignment)) +
    geom_boxplot() + coord_flip() + labs(x = "Round", y = "Distance to business grant (in days)",
    title = "Range of distance to business grant over time (Mentees)", subtitle = "Each round's distance to business grant") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "red") + scale_x_discrete(labels = c("Registration",
    "Baseline", "Follow-up 1", "Midline 3", "Midline 6", "Endline 9")) + theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels